A Photoplethysmography-Based Machine Learning Algorithm for Early Atrial Fibrillation Detection: A Prospective Validation Study

NCT07749183 | Recruiting DMC

• 1 other identifier: HeartCoreAF01
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

This is a prospective study validating a new machine-learning algorithm that detects atrial fibrillation (AF) from photoplethysmography (PPG) signals, developed for integration into the Seerlinq remote monitoring platform. This algorithm builds on the same core PPG signal-processing technology as Seerlinq's HeartCore device, a CE-certified (Class IIb, MDR) device that monitors left ventricular filling pressures in heart failure patients. The algorithm will be validated through internal cross-validation, external validation against an independent cohort with paired PPG-ECG recordings, and validation in a cohort of patients with paroxysmal atrial fibrillation and frequent sinus-AF transitions.

Conditions

Atrial Fibrillation (AF); Heart Failure

Keywords

Photoplethysmography; Machine learning; Remote telemonitoring; Wearable device; Digital biomarker; Arrhythmia detection

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy (area under the ROC curve) of the PPG-based machine-learning algorithm for detecting clinically relevant AF (≥ 30s), compared with gold-standard 12-lead ECG

  Through study completion (estimated November 2026)

Secondary Outcomes (8)

• Sensitivity and specificity of the algorithm at the Youden-optimal threshold

  Through study completion (estimated November 2026)

• Positive predictive value and negative predictive value

  Through study completion (estimated November 2026)

• Average precision

  Through study completion (estimated November 2026)

• Model calibration

  Through study completion (estimated November 2026)

• Matthews correlation coefficient

  Through study completion (estimated November 2026)

Study Arms (2)

Documented AF

HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment

Other: PPG-based AF detection algorithm

Non-AF

HF patients in sinus rhythm on the index 12-lead ECG with no prior documented AF episodes

Other: PPG-based AF detection algorithm

Interventions

PPG-based AF detection algorithm OTHER

The PPG-based atrial fibrillation detection algorithm is a non-invasive signal processing approach that analyzes photoplethysmographic waveforms obtained during remote monitoring. The algorithm evaluates pulse-to-pulse variability, waveform characteristics, and signal quality parameters to identify irregular rhythm patterns associated with atrial fibrillation and provide early detection of potential arrhythmic events.

Documented AF; Non-AF

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF) from Slovakia

You may qualify if:

• Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF)
• lead ECG performed to confirm cardiac rhythm classification (AF vs. non-AF)

You may not qualify if:

• Missing a valid PPG recording

Sponsors & Collaborators

• Seerlinq s. r. o.: lead
• ACADEMY - občianske združenie: collaborator
• Premedix Academy: collaborator

Study Sites (1)

Premedix

Bratislava, Slovakia

RECRUITING

MeSH Terms

Conditions

Atrial Fibrillation; Heart Failure

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Marta Kollárová, MSc., PhD.

CONTACT

+421 950 896 026; marta.kollarova@premedix.org

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 31, 2026
• First Posted: August 6, 2026
• Study Start: October 1, 2025
• Primary Completion: August 1, 2026
• Study Completion (Estimated): November 1, 2026
• Last Updated: August 6, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

SL (1)